"""any-llm backend for Headroom.

Talk to 38+ LLM providers (OpenAI, Mistral, Groq, Ollama, Bedrock, etc.)
through a single interface. Auth and format translation handled automatically.
"""

from __future__ import annotations

import json
import logging
import uuid
from collections.abc import AsyncIterator
from typing import Any, cast

from .base import Backend, BackendResponse, StreamEvent

logger = logging.getLogger(__name__)

try:
    from any_llm import AnyLLM

    ANYLLM_AVAILABLE = True
except ImportError:
    ANYLLM_AVAILABLE = False
    AnyLLM = None  # type: ignore


class AnyLLMBackend(Backend):
    """Backend using any-llm for multi-provider support."""

    def __init__(
        self,
        provider: str = "openai",
        api_key: str | None = None,
        api_base: str | None = None,
    ):
        if not ANYLLM_AVAILABLE:
            raise ImportError(
                "any-llm-sdk is required for AnyLLMBackend. "
                "Install with: pip install 'any-llm-sdk[all]'"
            )

        self.provider = provider.lower()
        # Normalize empty-string overrides (e.g. an env var set to "") to None
        # so provider defaults stay active instead of forwarding a blank value.
        self.api_key = api_key or None
        self.api_base = api_base or None

        # Create the AnyLLM instance once and reuse. api_key/api_base are only
        # forwarded when set so providers keep their own env-var defaults
        # (e.g. OPENAI_API_KEY / OPENAI_BASE_URL) otherwise.
        create_kwargs: dict[str, Any] = {}
        if self.api_key is not None:
            create_kwargs["api_key"] = self.api_key
        if self.api_base is not None:
            create_kwargs["api_base"] = self.api_base
        self.llm = AnyLLM.create(self.provider, **create_kwargs)

        logger.info(
            f"any-llm backend initialized (provider={provider}, "
            f"api_base={self.api_base or 'default'})"
        )

    @property
    def name(self) -> str:
        return f"anyllm-{self.provider}"

    def map_model_id(self, model: str) -> str:
        """Pass through model name - any-llm handles provider-specific naming."""
        return model

    def supports_model(self, model: str) -> bool:
        """any-llm supports any model the provider supports."""
        return True

    def _convert_messages(self, messages: list[dict[str, Any]]) -> list[dict[str, Any]]:
        """Convert Anthropic message format to OpenAI/any-llm format."""
        converted = []
        for msg in messages:
            role = msg.get("role", "user")
            content = msg.get("content", "")

            if isinstance(content, str):
                converted.append({"role": role, "content": content})
                continue

            if isinstance(content, list):
                text_parts = []
                has_complex_content = False

                for block in content:
                    if isinstance(block, dict):
                        if block.get("type") == "text":
                            text_parts.append(block.get("text", ""))
                        elif block.get("type") in ("tool_use", "tool_result", "image"):
                            has_complex_content = True
                            break

                if not has_complex_content and text_parts:
                    converted.append({"role": role, "content": "\n".join(text_parts)})
                else:
                    openai_content = self._convert_content_blocks(content)
                    converted.append({"role": role, "content": openai_content})

        return converted

    def _convert_content_blocks(self, blocks: list[dict[str, Any]]) -> list[dict[str, Any]] | str:
        """Convert Anthropic content blocks to OpenAI format."""
        openai_blocks = []

        for block in blocks:
            block_type = block.get("type")

            if block_type == "text":
                openai_blocks.append({"type": "text", "text": block.get("text", "")})

            elif block_type == "image":
                source = block.get("source", {})
                if source.get("type") == "base64":
                    media_type = source.get("media_type", "image/png")
                    data = source.get("data", "")
                    openai_blocks.append(
                        {
                            "type": "image_url",
                            "image_url": {"url": f"data:{media_type};base64,{data}"},
                        }
                    )
                elif source.get("type") == "url":
                    openai_blocks.append(
                        {"type": "image_url", "image_url": {"url": source.get("url", "")}}
                    )

        if len(openai_blocks) == 1 and openai_blocks[0].get("type") == "text":
            return str(openai_blocks[0]["text"])

        return openai_blocks if openai_blocks else ""

    def _to_anthropic_response(
        self,
        response: Any,
        original_model: str,
    ) -> dict[str, Any]:
        """Convert any-llm/OpenAI response to Anthropic format."""
        msg_id = f"msg_{uuid.uuid4().hex[:24]}"

        choice = response.choices[0]
        message = choice.message

        content = []
        if message.content:
            content.append({"type": "text", "text": message.content})

        if hasattr(message, "tool_calls") and message.tool_calls:
            for tc in message.tool_calls:
                args = tc.function.arguments
                if isinstance(args, str):
                    try:
                        args = json.loads(args)
                    except (json.JSONDecodeError, TypeError):
                        pass
                content.append(
                    {
                        "type": "tool_use",
                        "id": tc.id,
                        "name": tc.function.name,
                        "input": args,
                    }
                )

        stop_reason_map = {
            "stop": "end_turn",
            "length": "max_tokens",
            "tool_calls": "tool_use",
            "content_filter": "end_turn",
        }
        finish_reason = getattr(choice, "finish_reason", "stop") or "stop"
        stop_reason = stop_reason_map.get(finish_reason, "end_turn")

        usage = {"input_tokens": 0, "output_tokens": 0}
        if hasattr(response, "usage") and response.usage:
            usage = {
                "input_tokens": getattr(response.usage, "prompt_tokens", 0) or 0,
                "output_tokens": getattr(response.usage, "completion_tokens", 0) or 0,
            }

        return {
            "id": msg_id,
            "type": "message",
            "role": "assistant",
            "content": content,
            "model": original_model,
            "stop_reason": stop_reason,
            "stop_sequence": None,
            "usage": usage,
        }

    async def send_message(
        self,
        body: dict[str, Any],
        headers: dict[str, str],
    ) -> BackendResponse:
        """Send message via any-llm."""
        original_model = body.get("model", "gpt-4o")

        try:
            messages = self._convert_messages(body.get("messages", []))

            # Add system message if present
            if "system" in body:
                system = body["system"]
                if isinstance(system, str):
                    messages.insert(0, {"role": "system", "content": system})
                elif isinstance(system, list):
                    system_text = " ".join(
                        s.get("text", "") if isinstance(s, dict) else str(s) for s in system
                    )
                    messages.insert(0, {"role": "system", "content": system_text})

            kwargs: dict[str, Any] = {"model": original_model, "messages": messages}

            if "max_tokens" in body:
                kwargs["max_tokens"] = body["max_tokens"]
            if "temperature" in body:
                kwargs["temperature"] = body["temperature"]
            if "top_p" in body:
                kwargs["top_p"] = body["top_p"]
            if "stop_sequences" in body:
                kwargs["stop"] = body["stop_sequences"]
            if "tools" in body:
                kwargs["tools"] = body["tools"]
            if "tool_choice" in body:
                kwargs["tool_choice"] = body["tool_choice"]

            logger.debug(f"any-llm request: provider={self.provider}, model={original_model}")

            response = await self.llm.acompletion(**kwargs)
            anthropic_response = self._to_anthropic_response(response, original_model)

            return BackendResponse(
                body=anthropic_response,
                status_code=200,
                headers={"content-type": "application/json"},
            )

        except Exception as e:
            logger.error(f"any-llm error: {e}")
            return self._error_response(e)

    async def stream_message(
        self,
        body: dict[str, Any],
        headers: dict[str, str],
    ) -> AsyncIterator[StreamEvent]:
        """Stream message via any-llm."""
        original_model = body.get("model", "gpt-4o")

        try:
            messages = self._convert_messages(body.get("messages", []))

            if "system" in body:
                system = body["system"]
                if isinstance(system, str):
                    messages.insert(0, {"role": "system", "content": system})

            kwargs: dict[str, Any] = {
                "model": original_model,
                "messages": messages,
                "stream": True,
            }

            if "max_tokens" in body:
                kwargs["max_tokens"] = body["max_tokens"]
            if "temperature" in body:
                kwargs["temperature"] = body["temperature"]
            if "top_p" in body:
                kwargs["top_p"] = body["top_p"]
            if "stop_sequences" in body:
                kwargs["stop"] = body["stop_sequences"]
            if "tools" in body:
                kwargs["tools"] = body["tools"]
            if "tool_choice" in body:
                kwargs["tool_choice"] = body["tool_choice"]

            msg_id = f"msg_{uuid.uuid4().hex[:24]}"

            yield StreamEvent(
                event_type="message_start",
                data={
                    "type": "message_start",
                    "message": {
                        "id": msg_id,
                        "type": "message",
                        "role": "assistant",
                        "content": [],
                        "model": original_model,
                        "stop_reason": None,
                        "stop_sequence": None,
                        "usage": {"input_tokens": 0, "output_tokens": 0},
                    },
                },
            )

            yield StreamEvent(
                event_type="content_block_start",
                data={
                    "type": "content_block_start",
                    "index": 0,
                    "content_block": {"type": "text", "text": ""},
                },
            )

            stream_response = await self.llm.acompletion(**kwargs)
            output_tokens = 0

            async for chunk in cast(AsyncIterator[Any], stream_response):
                if hasattr(chunk, "choices") and chunk.choices:
                    delta = chunk.choices[0].delta
                    if hasattr(delta, "content") and delta.content:
                        yield StreamEvent(
                            event_type="content_block_delta",
                            data={
                                "type": "content_block_delta",
                                "index": 0,
                                "delta": {"type": "text_delta", "text": delta.content},
                            },
                        )
                        output_tokens += 1

            yield StreamEvent(
                event_type="content_block_stop",
                data={"type": "content_block_stop", "index": 0},
            )

            yield StreamEvent(
                event_type="message_delta",
                data={
                    "type": "message_delta",
                    "delta": {"stop_reason": "end_turn", "stop_sequence": None},
                    "usage": {"output_tokens": output_tokens},
                },
            )

            yield StreamEvent(
                event_type="message_stop",
                data={"type": "message_stop"},
            )

        except Exception as e:
            logger.error(f"any-llm streaming error: {e}")
            yield StreamEvent(
                event_type="error",
                data={
                    "type": "error",
                    "error": {"type": "api_error", "message": str(e)},
                },
            )

    async def send_openai_message(
        self,
        body: dict[str, Any],
        headers: dict[str, str],
    ) -> BackendResponse:
        """Send OpenAI-format message via any-llm (no conversion)."""
        original_model = body.get("model", "gpt-4o")

        try:
            kwargs: dict[str, Any] = {"model": original_model, "messages": body.get("messages", [])}

            for param in [
                "max_tokens",
                "temperature",
                "top_p",
                "stop",
                "tools",
                "tool_choice",
                "response_format",
                "seed",
                "n",
            ]:
                if param in body:
                    kwargs[param] = body[param]

            logger.debug(
                f"any-llm OpenAI request: provider={self.provider}, model={original_model}"
            )

            response: Any = await self.llm.acompletion(**kwargs)

            choices = []
            for c in response.choices:
                msg: dict[str, Any] = {
                    "role": c.message.role,
                    "content": c.message.content,
                }
                if c.message.tool_calls:
                    msg["tool_calls"] = [
                        {
                            "id": tc.id,
                            "type": "function",
                            **(
                                {
                                    "function": {
                                        "name": tc.function.name,
                                        "arguments": tc.function.arguments,
                                    }
                                }
                                if hasattr(tc, "function") and tc.function
                                else {}
                            ),
                        }
                        for tc in c.message.tool_calls
                    ]
                choices.append(
                    {
                        "index": c.index,
                        "message": msg,
                        "finish_reason": c.finish_reason,
                    }
                )

            usage = response.usage
            response_dict: dict[str, Any] = {
                "id": response.id,
                "object": "chat.completion",
                "created": response.created,
                "model": original_model,
                "choices": choices,
                "usage": {
                    "prompt_tokens": usage.prompt_tokens if usage else 0,
                    "completion_tokens": usage.completion_tokens if usage else 0,
                    "total_tokens": usage.total_tokens if usage else 0,
                },
            }

            return BackendResponse(
                body=response_dict,
                status_code=200,
                headers={"content-type": "application/json"},
            )

        except Exception as e:
            logger.error(f"any-llm OpenAI error: {e}")
            return self._error_response(e, openai_format=True)

    def _error_response(self, e: Exception, openai_format: bool = False) -> BackendResponse:
        """Build error response from exception."""
        error_type = "api_error"
        status_code = 500

        error_str = str(e).lower()
        if "authentication" in error_str or "api_key" in error_str or "api key" in error_str:
            error_type = "invalid_api_key" if openai_format else "authentication_error"
            status_code = 401
        elif "rate" in error_str or "limit" in error_str:
            error_type = "rate_limit_exceeded" if openai_format else "rate_limit_error"
            status_code = 429
        elif "not found" in error_str or "model" in error_str:
            error_type = "model_not_found" if openai_format else "not_found_error"
            status_code = 404

        body: dict[str, Any]
        if openai_format:
            body = {"error": {"message": str(e), "type": error_type, "code": error_type}}
        else:
            body = {"type": "error", "error": {"type": error_type, "message": str(e)}}

        return BackendResponse(body=body, status_code=status_code, error=str(e))

    async def stream_openai_message(
        self,
        body: dict[str, Any],
        headers: dict[str, str],
    ) -> AsyncIterator[str]:
        """Stream OpenAI-format chat completion via any-llm.

        Yields SSE-formatted strings ready to send to the client.
        """
        original_model = body.get("model", "gpt-4o")

        try:
            kwargs: dict[str, Any] = {
                "model": original_model,
                "messages": body.get("messages", []),
                "stream": True,
            }

            for param in [
                "max_tokens",
                "temperature",
                "top_p",
                "stop",
                "tools",
                "tool_choice",
                "response_format",
                "seed",
                "n",
            ]:
                if param in body:
                    kwargs[param] = body[param]

            if "stream_options" in body:
                kwargs["stream_options"] = body["stream_options"]

            stream_response = await self.llm.acompletion(**kwargs)

            async for chunk in cast(AsyncIterator[Any], stream_response):
                chunk_dict = chunk.model_dump(exclude_none=True, exclude_unset=True)
                yield f"data: {json.dumps(chunk_dict)}\n\n"

            yield "data: [DONE]\n\n"

        except Exception as e:
            logger.error(f"any-llm OpenAI streaming error: {e}")
            error_data = {
                "error": {
                    "message": str(e),
                    "type": "api_error",
                    "code": "backend_error",
                }
            }
            yield f"data: {json.dumps(error_data)}\n\n"
            yield "data: [DONE]\n\n"

    async def close(self) -> None:
        """Clean up (no-op for any-llm)."""
        pass